Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-30T23:38:48.406Z Has data issue: false hasContentIssue false

Macronutrient intake and prevalence of markers of metabolic syndrome in white UK adult males in the National Diet and Nutrition Survey Rolling Programme 2008–2014

Published online by Cambridge University Press:  11 December 2017

T. Harrison
Affiliation:
School of Sport Studies, Leisure and Nutrition, Liverpool John Moores University, Liverpool, L17 6BD
K.E. Lane
Affiliation:
School of Sport Studies, Leisure and Nutrition, Liverpool John Moores University, Liverpool, L17 6BD
L.M. Boddy
Affiliation:
School of Sports and Exercise Sciences, Liverpool John Moores University, Liverpool, L3 3AF
F. Amirabdollahian
Affiliation:
School of Health Sciences, Liverpool Hope University, Liverpool, L16 9JD
I.G. Davies
Affiliation:
School of Sport Studies, Leisure and Nutrition, Liverpool John Moores University, Liverpool, L17 6BD
Rights & Permissions [Opens in a new window]

Abstract

Type
Abstract
Copyright
Copyright © The Authors 2017 

The amount of carbohydrates recommended for consumption by current dietary guidelines has been challenged in relation to their suitability to prevent or manage cardiometabolic (CM) diseases with suggestions that they should be decreased and replaced by protein or fat( Reference Henderson, Zinn and Schofield 1 , Reference Hu and Bazzano 2 ). Others have argued that a more personalised approach is required( Reference Noecker and Borenstein 3 ). Aim of this investigation was to assess the potential impact of lower versus higher consumption of dietary macronutrients and prevalence of CM risk markers in a representative sample of the UK male white population.

Unweighted data from 642 white adult males aged 19 and over in the National Diet and Nutrition Survey Rolling Programme( 4 ) (NDNS RP) 2008–2014 with or without metabolic syndrome (MetS)( Reference Alberti, Eckel and Grundy 5 ) were analysed for associations of dietary macronutrient intake as percentage food energy (%FE) with CM risk markers. Logistic regression analysis (adjusted for age group and smoking status) was used to compare the odds ratios [OR] of prevalence of individual markers of MetS between the lowest and highest quartiles of dietary macronutrient intake as %FE (⩽44 vs. ≥52 for carbohydrates; ⩽31 vs. ≥39 for fats; ⩽15. vs. ≥19 for protein).

There was a significant (p < 0·05) reduction in likelihood of MetS (OR, .55; 95 % confidence interval [CI], .34 to .84), and elevated waist circumference (OR, .50; 95 % CI, .30 to .83) and glucose levels (OR, .51; 95 % CI, .30 to .87) for those in the highest quartile of carbohydrate %FE intake compared to the lowest quartile, whereas those in the highest quartile of protein %FE intake had a significantly (p < 0·05) increased risk of presenting with the same markers of MetS (OR, 1·75; 95 % CI, 1·05 to 2·93; OR, 2·12; 95 % CI, 1·24 to 3·63; and OR, 2·15; 95 % CI, 1·25 to 3·70 respectively). Those with the highest compared to the lowest total dietary fat intake also presented with elevated CM risk markers, albeit these findings were not significant.

*Metabolic Syndrome (MetS) definition: 3 out of 5 of the following: triglycerides (TRIG) ≥1·7 mmol/L; High-density lipoprotein cholesterol (HDL-C) ⩽1·03 mmol/L for males; Waist circumference (WC) ≥94 cm for white males; Glucose (GLUC) ≥5·6 mmol/L; Blood pressure (BP) ≥130 mmHg systolic or ≥85 mmHg diastolic respectively; CHO%FE – total carbohydrates percentage food energy; FAT%FE – total fats food energy; PROT%FE– total protein food energy; OR – odds ratio (adjusted for age group and smoking status), 1st vs. 4th quartile of intake; CI – confidence interval; a p < 0·05

Further investigations need to confirm whether the quality of the macronutrients consumed and overall diet quality( Reference Schwingshackl and Hoffmann 6 ) has had an impact on these results. In the context of a personalised approach to nutrition future cohort studies should also provide data that allow for examining inter-individual variations in responses to dietary macronutrients, especially carbohydrates, to achieve optimum CM health for a larger proportion of the population.

References

1. Henderson, G, Zinn, C & Schofield, G (2017) Lancet 389, 589.CrossRefGoogle Scholar
2. Hu, T & Bazzano, LA (2014) Nutr Metab Cardiovasc Dis 24, 337–43.CrossRefGoogle Scholar
3. Noecker, C & Borenstein, E (2016) Trends Mol Med 22, 8385.CrossRefGoogle Scholar
5. Alberti, KG, Eckel, RH, Grundy, SM et al. (2009) Circulation 120, 1640–5.CrossRefGoogle Scholar
6. Schwingshackl, L & Hoffmann, G (2015) J Acad Nutr Diet 115, 780800.CrossRefGoogle ScholarPubMed